Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Text1
Categorical2
Numeric6
DateTime2

Alerts

gross margin percentage has constant value ""Constant
Invoice ID has unique valuesUnique

Reproduction

Analysis started2024-02-21 00:10:45.574367
Analysis finished2024-02-21 00:10:55.764225
Duration10.19 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Invoice ID
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-02-21T05:40:56.303416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row750-67-8428
2nd row226-31-3081
3rd row631-41-3108
4th row123-19-1176
5th row373-73-7910
ValueCountFrequency (%)
750-67-8428 1
 
0.1%
252-56-2699 1
 
0.1%
871-79-8483 1
 
0.1%
848-62-7243 1
 
0.1%
631-41-3108 1
 
0.1%
123-19-1176 1
 
0.1%
373-73-7910 1
 
0.1%
699-14-3026 1
 
0.1%
355-53-5943 1
 
0.1%
315-22-5665 1
 
0.1%
Other values (990) 990
99.0%
2024-02-21T05:40:57.394205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9000
81.8%
Dash Punctuation 2000
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 957
10.6%
6 954
10.6%
1 950
10.6%
8 944
10.5%
5 927
10.3%
4 918
10.2%
3 909
10.1%
7 895
9.9%
0 809
9.0%
9 737
8.2%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2000
18.2%
2 957
8.7%
6 954
8.7%
1 950
8.6%
8 944
8.6%
5 927
8.4%
4 918
8.3%
3 909
8.3%
7 895
8.1%
0 809
7.4%

Product line
Categorical

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Watch
94 
Sunglasses
84 
Yoga mat
 
63
Coffee
 
60
Chocolate
 
58
Other values (14)
641 

Length

Max length10
Median length8
Mean length6.701
Min length3

Characters and Unicode

Total characters6701
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoap
2nd rowPhone case
3rd rowVase
4th rowLotion
5th rowBottle

Common Values

ValueCountFrequency (%)
Watch 94
 
9.4%
Sunglasses 84
 
8.4%
Yoga mat 63
 
6.3%
Coffee 60
 
6.0%
Chocolate 58
 
5.8%
Vase 56
 
5.6%
Bottle 56
 
5.6%
Tea 56
 
5.6%
Towel 53
 
5.3%
Candle 51
 
5.1%
Other values (9) 369
36.9%

Length

2024-02-21T05:40:57.998952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
watch 94
 
8.2%
sunglasses 84
 
7.3%
yoga 63
 
5.5%
mat 63
 
5.5%
coffee 60
 
5.2%
chocolate 58
 
5.1%
vase 56
 
4.9%
bottle 56
 
4.9%
tea 56
 
4.9%
towel 53
 
4.6%
Other values (12) 503
43.9%

Most occurring characters

ValueCountFrequency (%)
e 888
13.3%
a 811
 
12.1%
o 644
 
9.6%
t 438
 
6.5%
s 391
 
5.8%
h 309
 
4.6%
l 302
 
4.5%
S 293
 
4.4%
n 247
 
3.7%
c 229
 
3.4%
Other values (20) 2149
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5555
82.9%
Uppercase Letter 1000
 
14.9%
Space Separator 146
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 888
16.0%
a 811
14.6%
o 644
11.6%
t 438
 
7.9%
s 391
 
7.0%
h 309
 
5.6%
l 302
 
5.4%
n 247
 
4.4%
c 229
 
4.1%
m 185
 
3.3%
Other values (10) 1111
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 293
29.3%
C 169
16.9%
P 131
13.1%
T 109
 
10.9%
W 94
 
9.4%
Y 63
 
6.3%
B 56
 
5.6%
V 56
 
5.6%
L 29
 
2.9%
Space Separator
ValueCountFrequency (%)
146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6555
97.8%
Common 146
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 888
13.5%
a 811
12.4%
o 644
 
9.8%
t 438
 
6.7%
s 391
 
6.0%
h 309
 
4.7%
l 302
 
4.6%
S 293
 
4.5%
n 247
 
3.8%
c 229
 
3.5%
Other values (19) 2003
30.6%
Common
ValueCountFrequency (%)
146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 888
13.3%
a 811
 
12.1%
o 644
 
9.6%
t 438
 
6.5%
s 391
 
5.8%
h 309
 
4.6%
l 302
 
4.5%
S 293
 
4.4%
n 247
 
3.7%
c 229
 
3.4%
Other values (20) 2149
32.1%

Unit price
Real number (ℝ)

Distinct943
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.67213
Minimum10.08
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:40:58.801447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10.08
5-th percentile15.279
Q132.875
median55.23
Q377.935
95-th percentile97.222
Maximum99.96
Range89.88
Interquartile range (IQR)45.06

Descriptive statistics

Standard deviation26.494628
Coefficient of variation (CV)0.4759047
Kurtosis-1.2185914
Mean55.67213
Median Absolute Deviation (MAD)22.505
Skewness0.0070774479
Sum55672.13
Variance701.96533
MonotonicityNot monotonic
2024-02-21T05:40:59.525972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.77 3
 
0.3%
39.62 2
 
0.2%
24.74 2
 
0.2%
19.15 2
 
0.2%
73.47 2
 
0.2%
95.54 2
 
0.2%
78.31 2
 
0.2%
26.26 2
 
0.2%
89.48 2
 
0.2%
72.88 2
 
0.2%
Other values (933) 979
97.9%
ValueCountFrequency (%)
10.08 1
0.1%
10.13 1
0.1%
10.16 1
0.1%
10.17 1
0.1%
10.18 1
0.1%
10.53 1
0.1%
10.56 1
0.1%
10.59 1
0.1%
10.69 1
0.1%
10.75 1
0.1%
ValueCountFrequency (%)
99.96 2
0.2%
99.92 1
0.1%
99.89 1
0.1%
99.83 1
0.1%
99.82 2
0.2%
99.79 1
0.1%
99.78 1
0.1%
99.73 1
0.1%
99.71 1
0.1%
99.7 1
0.1%

Quantity
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.51
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:40:59.814246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9234306
Coefficient of variation (CV)0.53056817
Kurtosis-1.2155472
Mean5.51
Median Absolute Deviation (MAD)2
Skewness0.012941048
Sum5510
Variance8.5464464
MonotonicityNot monotonic
2024-02-21T05:41:00.222403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 119
11.9%
1 112
11.2%
4 109
10.9%
7 102
10.2%
5 102
10.2%
6 98
9.8%
9 92
9.2%
2 91
9.1%
3 90
9.0%
8 85
8.5%
ValueCountFrequency (%)
1 112
11.2%
2 91
9.1%
3 90
9.0%
4 109
10.9%
5 102
10.2%
6 98
9.8%
7 102
10.2%
8 85
8.5%
9 92
9.2%
10 119
11.9%
ValueCountFrequency (%)
10 119
11.9%
9 92
9.2%
8 85
8.5%
7 102
10.2%
6 98
9.8%
5 102
10.2%
4 109
10.9%
3 90
9.0%
2 91
9.1%
1 112
11.2%

Total
Real number (ℝ)

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.96675
Minimum10.6785
Maximum1042.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:41:00.955377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10.6785
5-th percentile41.070225
Q1124.42238
median253.848
Q3471.35025
95-th percentile822.4965
Maximum1042.65
Range1031.9715
Interquartile range (IQR)346.92787

Descriptive statistics

Standard deviation245.88534
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean322.96675
Median Absolute Deviation (MAD)157.68375
Skewness0.8925698
Sum322966.75
Variance60459.598
MonotonicityNot monotonic
2024-02-21T05:41:01.520327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216.846 2
 
0.2%
93.744 2
 
0.2%
87.234 2
 
0.2%
189.0945 2
 
0.2%
470.988 2
 
0.2%
829.08 2
 
0.2%
217.6335 2
 
0.2%
175.917 2
 
0.2%
276.948 2
 
0.2%
263.97 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
10.6785 1
0.1%
12.6945 1
0.1%
13.167 1
0.1%
13.419 1
0.1%
14.679 1
0.1%
16.107 1
0.1%
16.2015 1
0.1%
16.275 1
0.1%
17.094 1
0.1%
18.6375 1
0.1%
ValueCountFrequency (%)
1042.65 1
0.1%
1039.29 1
0.1%
1034.46 1
0.1%
1023.75 1
0.1%
1022.49 1
0.1%
1022.385 1
0.1%
1020.705 1
0.1%
1003.59 1
0.1%
1002.12 1
0.1%
951.825 1
0.1%

Date
Date

Distinct91
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2019-01-01 00:00:00
Maximum2019-10-10 00:00:00
2024-02-21T05:41:02.093530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:41:02.557451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct506
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2024-02-21 10:00:00
Maximum2024-02-21 20:59:00
2024-02-21T05:41:03.052224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:41:03.671995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cogs
Real number (ℝ)

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.58738
Minimum10.17
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:41:04.496633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10.17
5-th percentile39.1145
Q1118.4975
median241.76
Q3448.905
95-th percentile783.33
Maximum993
Range982.83
Interquartile range (IQR)330.4075

Descriptive statistics

Standard deviation234.17651
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean307.58738
Median Absolute Deviation (MAD)150.175
Skewness0.8925698
Sum307587.38
Variance54838.638
MonotonicityNot monotonic
2024-02-21T05:41:04.995797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.52 2
 
0.2%
89.28 2
 
0.2%
83.08 2
 
0.2%
180.09 2
 
0.2%
448.56 2
 
0.2%
789.6 2
 
0.2%
207.27 2
 
0.2%
167.54 2
 
0.2%
263.76 2
 
0.2%
251.4 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
10.17 1
0.1%
12.09 1
0.1%
12.54 1
0.1%
12.78 1
0.1%
13.98 1
0.1%
15.34 1
0.1%
15.43 1
0.1%
15.5 1
0.1%
16.28 1
0.1%
17.75 1
0.1%
ValueCountFrequency (%)
993 1
0.1%
989.8 1
0.1%
985.2 1
0.1%
975 1
0.1%
973.8 1
0.1%
973.7 1
0.1%
972.1 1
0.1%
955.8 1
0.1%
954.4 1
0.1%
906.5 1
0.1%

gross margin percentage
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
4.761904762
1000 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.761904762
2nd row4.761904762
3rd row4.761904762
4th row4.761904762
5th row4.761904762

Common Values

ValueCountFrequency (%)
4.761904762 1000
100.0%

Length

2024-02-21T05:41:05.580060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T05:41:05.990588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.761904762 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
90.9%
Other Punctuation 1000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2000
20.0%
7 2000
20.0%
6 2000
20.0%
1 1000
10.0%
9 1000
10.0%
0 1000
10.0%
2 1000
10.0%
Other Punctuation
ValueCountFrequency (%)
. 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2000
18.2%
7 2000
18.2%
6 2000
18.2%
. 1000
9.1%
1 1000
9.1%
9 1000
9.1%
0 1000
9.1%
2 1000
9.1%

gross income
Real number (ℝ)

Distinct990
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.379369
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:41:06.430287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.955725
Q15.924875
median12.088
Q322.44525
95-th percentile39.1665
Maximum49.65
Range49.1415
Interquartile range (IQR)16.520375

Descriptive statistics

Standard deviation11.708825
Coefficient of variation (CV)0.76133328
Kurtosis-0.081884758
Mean15.379369
Median Absolute Deviation (MAD)7.50875
Skewness0.8925698
Sum15379.369
Variance137.09659
MonotonicityNot monotonic
2024-02-21T05:41:07.407075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.326 2
 
0.2%
4.464 2
 
0.2%
4.154 2
 
0.2%
9.0045 2
 
0.2%
22.428 2
 
0.2%
39.48 2
 
0.2%
10.3635 2
 
0.2%
8.377 2
 
0.2%
13.188 2
 
0.2%
12.57 2
 
0.2%
Other values (980) 980
98.0%
ValueCountFrequency (%)
0.5085 1
0.1%
0.6045 1
0.1%
0.627 1
0.1%
0.639 1
0.1%
0.699 1
0.1%
0.767 1
0.1%
0.7715 1
0.1%
0.775 1
0.1%
0.814 1
0.1%
0.8875 1
0.1%
ValueCountFrequency (%)
49.65 1
0.1%
49.49 1
0.1%
49.26 1
0.1%
48.75 1
0.1%
48.69 1
0.1%
48.685 1
0.1%
48.605 1
0.1%
47.79 1
0.1%
47.72 1
0.1%
45.325 1
0.1%

Rating
Real number (ℝ)

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9727
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-21T05:41:08.791450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.295
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7185803
Coefficient of variation (CV)0.24647271
Kurtosis-1.1515868
Mean6.9727
Median Absolute Deviation (MAD)1.5
Skewness0.0090096488
Sum6972.7
Variance2.9535182
MonotonicityNot monotonic
2024-02-21T05:41:09.828604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 26
 
2.6%
6.6 24
 
2.4%
4.2 22
 
2.2%
9.5 22
 
2.2%
6.5 21
 
2.1%
5 21
 
2.1%
6.2 21
 
2.1%
8 21
 
2.1%
5.1 21
 
2.1%
7.6 20
 
2.0%
Other values (51) 781
78.1%
ValueCountFrequency (%)
4 11
1.1%
4.1 17
1.7%
4.2 22
2.2%
4.3 18
1.8%
4.4 17
1.7%
4.5 17
1.7%
4.6 8
 
0.8%
4.7 12
1.2%
4.8 13
1.3%
4.9 18
1.8%
ValueCountFrequency (%)
10 5
 
0.5%
9.9 16
1.6%
9.8 19
1.9%
9.7 14
1.4%
9.6 17
1.7%
9.5 22
2.2%
9.4 12
1.2%
9.3 16
1.6%
9.2 16
1.6%
9.1 14
1.4%

Interactions

2024-02-21T05:40:52.590289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:46.467733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.367219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:48.696187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:49.810928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.995134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:52.892993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:46.622326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.489204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:48.881784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.056446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:51.352474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:53.315004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:46.752348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.745167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:49.148916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.266003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:51.623061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:53.604324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:46.895509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.965312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:49.304303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.477395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:51.832329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:54.010971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.037707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:48.140658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:49.450661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.643934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:52.076759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:54.361530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:47.194626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:48.447466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:49.629635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:50.804360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-21T05:40:52.263344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-21T05:40:54.717769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T05:40:55.486669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Invoice IDProduct lineUnit priceQuantityTotalDateTimecogsgross margin percentagegross incomeRating
0750-67-8428Soap74.697548.971501-05-201913:08522.834.76190526.14159.1
1226-31-3081Phone case15.28580.220003-08-201910:2976.404.7619053.82009.6
2631-41-3108Vase46.337340.525503-03-201913:23324.314.76190516.21557.4
3123-19-1176Lotion58.228489.04801-27-201920:33465.764.76190523.28808.4
4373-73-7910Bottle86.317634.378502-08-201910:37604.174.76190530.20855.3
5699-14-3026Smartwatch85.397627.61653-25-201918:30597.734.76190529.88654.1
6355-53-5943Speaker68.846433.69202-25-201914:36413.044.76190520.65205.8
7315-22-5665Candle73.5610772.38002-24-201911:38735.604.76190536.78008.0
8665-32-9167Soap36.26276.146010-10-201917:1572.524.7619053.62607.2
9692-92-5582Chocolate54.843172.74602-20-201913:27164.524.7619058.22605.9
Invoice IDProduct lineUnit priceQuantityTotalDateTimecogsgross margin percentagegross incomeRating
990886-18-2897Coffee56.565296.94003-22-201919:06282.804.76190514.14004.5
991602-16-6955Bottle76.6010804.30001-24-201918:10766.004.76190538.30006.0
992745-74-0715Power bank58.032121.863003-10-201920:46116.064.7619055.80308.8
993690-01-6631Watch17.4910183.64502-22-201918:35174.904.7619058.74506.6
994652-49-6720Power bank60.95163.99752-18-201911:4060.954.7619053.04755.9
995233-67-5758Perfume40.35142.36751-29-201913:4640.354.7619052.01756.2
996303-96-2227Towel97.38101022.490003-02-201917:16973.804.76190548.69004.4
997727-02-1313Tea31.84133.432002-09-201913:2231.844.7619051.59207.7
998347-56-2442Candle65.82169.11102-22-201915:3365.824.7619053.29104.1
999849-09-3807Sunglasses88.347649.29902-18-201913:28618.384.76190530.91906.6